Input or training data is incredibly important for computer-based reasoning systems. Even when there are sufficient cases (e.g., training data) to make a useful computer-based reasoning model, the data for each of the cases may lack some of the data items (fields) within the case. Consider, for example, a data set collected over a long period of time for oil pumps. Earlier oil pumps may have lacked many of the sensors and data collection mechanisms used on later oil pumps. Further, even if the older oil pumps are retrofitted with the sensors and data collection mechanisms used in more modern pumps, the data collected before those additional sensors and collection mechanisms will lack the data associated with those later-added sensors/mechanisms. As such, this earlier data cannot be used for training computer-based reasoning models.
The techniques herein overcome these issues.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.